





'''simple docstring'''






import argparse
import gc
import json
import os
import re

import torch
from huggingface_hub import hf_hub_download

from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint


lowerCAmelCase									=   {
    """169M""": 12,
    """430M""": 24,
    """1B5""": 24,
    """3B""": 32,
    """7B""": 32,
    """14B""": 40,
}

lowerCAmelCase									=   {
    """169M""": 7_68,
    """430M""": 10_24,
    """1B5""": 20_48,
    """3B""": 25_60,
    """7B""": 40_96,
    """14B""": 51_20,
}






def   __A    (				a_    :	Any						):
		lowerCAmelCase  :     Tuple       						= list(state_dict.keys()						)
		for name in state_dict_keys:
				lowerCAmelCase  :     Dict       						= state_dict.pop(a_						)
				# emb -> embedding
				if name.startswith("emb."						):
						lowerCAmelCase  :     List[Any]       						= name.replace("emb."   ,"embeddings."						)
				# ln_0 -> pre_ln (only present at block 0)
				if name.startswith("blocks.0.ln0"						):
						lowerCAmelCase  :     int       						= name.replace("blocks.0.ln0"   ,"blocks.0.pre_ln"						)
				# att -> attention
				lowerCAmelCase  :     List[Any]       						= re.sub(R"blocks\.(\d+)\.att"   ,R"blocks.\1.attention"   ,a_						)
				# ffn -> feed_forward
				lowerCAmelCase  :     Any       						= re.sub(R"blocks\.(\d+)\.ffn"   ,R"blocks.\1.feed_forward"   ,a_						)
				# time_mix_k -> time_mix_key and reshape
				if name.endswith(".time_mix_k"						):
						lowerCAmelCase  :     List[Any]       						= name.replace(".time_mix_k"   ,".time_mix_key"						)
				# time_mix_v -> time_mix_value and reshape
				if name.endswith(".time_mix_v"						):
						lowerCAmelCase  :     Optional[int]       						= name.replace(".time_mix_v"   ,".time_mix_value"						)
				# time_mix_r -> time_mix_key and reshape
				if name.endswith(".time_mix_r"						):
						lowerCAmelCase  :     Any       						= name.replace(".time_mix_r"   ,".time_mix_receptance"						)

				if name != "head.weight":
						lowerCAmelCase  :     str       						= "rwkv." + name

				lowerCAmelCase  :     Tuple       						= weight
		return state_dict






def   __A    (				a_    :	Any   ,a_    :	Any   ,a_    :	List[Any]   ,a_    :	int=None   ,a_    :	str=None   ,a_    :	Any=False   ,a_    :	Dict=None						):
		# 1. If possible, build the tokenizer.
		if tokenizer_file is None:
				print("No `--tokenizer_file` provided, we will use the default tokenizer."						)
				lowerCAmelCase  :     Optional[Any]       						= 5_0_2_7_7
				lowerCAmelCase  :     Optional[Any]       						= AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b"						)
		else:
				lowerCAmelCase  :     Dict       						= PreTrainedTokenizerFast(tokenizer_file=a_						)
				lowerCAmelCase  :     Optional[Any]       						= len(a_						)
		tokenizer.save_pretrained(a_						)

		# 2. Build the config
		lowerCAmelCase  :     str       						= list(NUM_HIDDEN_LAYERS_MAPPING.keys()						)
		if size is None:
				# Try to infer size from the checkpoint name
				for candidate in possible_sizes:
						if candidate in checkpoint_file:
								lowerCAmelCase  :     Optional[int]       						= candidate
								break
				if size is None:
						raise ValueError("Could not infer the size, please provide it with the `--size` argument."						)
		if size not in possible_sizes:
				raise ValueError(f'''`size` should be one of {possible_sizes}, got {size}.'''						)

		lowerCAmelCase  :     int       						= RwkvConfig(
		    vocab_size=a_   ,num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size]   ,hidden_size=HIDEN_SIZE_MAPPING[size]   ,)
		config.save_pretrained(a_						)

		# 3. Download model file then convert state_dict
		lowerCAmelCase  :     List[str]       						= hf_hub_download(a_   ,a_						)
		lowerCAmelCase  :     List[str]       						= torch.load(a_   ,map_location="cpu"						)
		lowerCAmelCase  :     Optional[Any]       						= convert_state_dict(a_						)

		# 4. Split in shards and save
		lowerCAmelCase						,		lowerCAmelCase  :     Union[str, Any]       						= shard_checkpoint(a_						)
		for shard_file, shard in shards.items():
				torch.save(a_   ,os.path.join(a_   ,a_						)						)

		if index is not None:
				lowerCAmelCase  :     List[str]       						= os.path.join(a_   ,a_						)
				# Save the index as well
				with open(a_   ,"w"   ,encoding="utf-8"						) as f:
						lowerCAmelCase  :     Union[str, Any]       						= json.dumps(a_   ,indent=2   ,sort_keys=a_						) + "\n"
						f.write(a_						)

				# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
				print(
				    "Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model."						)
				lowerCAmelCase  :     List[Any]       						= list(shards.keys()						)

				del state_dict
				del shards
				gc.collect()

				for shard_file in shard_files:
						lowerCAmelCase  :     Dict       						= torch.load(os.path.join(a_   ,a_						)						)
						torch.save({k: v.cpu().clone() for k, v in state_dict.items()}   ,os.path.join(a_   ,a_						)						)

		del state_dict
		gc.collect()

		if push_to_hub:
				if model_name is None:
						raise ValueError("Please provide a `model_name` to push the model to the Hub."						)
				lowerCAmelCase  :     Any       						= AutoModelForCausalLM.from_pretrained(a_						)
				model.push_to_hub(a_   ,max_shard_size="2GB"						)
				tokenizer.push_to_hub(a_						)


if __name__ == "__main__":
					lowerCAmelCase									=   argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
					)
					parser.add_argument(
					    """--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
					)
					parser.add_argument(
					    """--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
					)
					parser.add_argument(
					    """--tokenizer_file""",
					    default=None,
					    type=str,
					    help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
					)
					parser.add_argument(
					    """--size""",
					    default=None,
					    type=str,
					    help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
					)
					parser.add_argument(
					    """--push_to_hub""",
					    action="""store_true""",
					    help="""Push to the Hub the converted model.""",
					)
					parser.add_argument(
					    """--model_name""",
					    default=None,
					    type=str,
					    help="""Name of the pushed model on the Hub, including the username / organization.""",
					)

					lowerCAmelCase									=   parser.parse_args()
					convert_rmkv_checkpoint_to_hf_format(
					    args.repo_id,
					    args.checkpoint_file,
					    args.output_dir,
					    size=args.size,
					    tokenizer_file=args.tokenizer_file,
					    push_to_hub=args.push_to_hub,
					    model_name=args.model_name,
					)










'''simple docstring'''






import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer






def   __A    (				a_    :	str   ,a_    :	str   ,**a_    :	int						):
		lowerCAmelCase  :     str       						= AutoConfig.from_pretrained(a_   ,**a_						)
		lowerCAmelCase  :     Any       						= AutoModelForSeqaSeqLM.from_config(a_						)
		model.save_pretrained(a_						)
		AutoTokenizer.from_pretrained(a_						).save_pretrained(a_						)
		return model


if __name__ == "__main__":
					fire.Fire(save_randomly_initialized_version)



